A Data-Driven Approach for Condition Monitoring of Wind Turbine Pitch Systems

Cong Yang, Zheng Qian, Yan Pei and Lu Wei
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Cong Yang: School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100083, China
Zheng Qian: School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100083, China
Yan Pei: State Key Laboratory of Operation and Control of Renewable Energy Storage Systems, China Electric Power Research Institute, Beijing 100192, China
Lu Wei: School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100083, China

Energies, 2018, vol. 11, issue 8, 1-17

Abstract: With the rapid development of wind energy, it is important to reduce operation and maintenance (O&M) costs of wind turbines (WTs), especially for a pitch system, which suffers the highest failure rate and downtime. This paper proposes a data-driven method for pitch- system condition monitoring (CM) by only using supervisory control and data acquisition (SCADA) data without any faults, which could be applied to reduce O&M costs of pitch system by providing fault alarms. The pitch-motor temperature is selected as the indicator, and three feature-selection algorithms are employed to select the most appropriate input parameters for modeling. Six data-driven algorithms are applied to model pitch-motor temperature and the support vector regression (SVR) model has the highest accuracy. The control-chart method based on the residual errors between model output and measured value is utilized to calculate the outliers, thus the abnormal condition could be clearly identified once the outliers appear for a period of time. The effectiveness of the proposed method is demonstrated by several case studies, and compared with the classification models. Due to the adaptive ability and low cost, the proposed approach is suitable for online CM of pitch systems, and provides a strategy for CM of new WTs.

Keywords: pitch system; condition monitoring; SVR; feature selection; control chart (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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